Torque feedback based vehicle longitudinal automatic calibration system for autonomous driving vehicles

ABSTRACT

A calibration table usable in operating an autonomous driving vehicle (ADV) is updated. The operations comprise: determining a first torque value at a first time instant prior to executing a control command; determining a control command based on a speed of the ADV, a desired acceleration, and an associated entry in the calibration table; executing the control command; determining a second torque value at a second time instant subsequent to executing the control command; determining a torque error value as a difference between the first and second torque values; updating the associated entry in the calibration table based at least in part on the torque error value; and generating driving signals based at least in part on the updated calibration table to control operations of the ADV.

CROSS-REFERENCE TO RELATED APPLICATION

This patent application is a U.S. National Phase Application under 35U.S.C. § 371 of International Application No. PCT/CN2018/123962, filedDec. 26, 2018, entitled “TORQUE FEEDBACK BASED VEHICLE LONGITUDINALAUTOMATIC CALIBRATION SYSTEM FOR AUTONOMOUS DRIVING VEHICLES,” which isincorporated by reference herein by its entirety.

TECHNICAL FIELD

Embodiments of the present disclosure relate generally to operatingautonomous vehicles. More particularly, embodiments of the disclosurerelate to automatic calibration in the control system of an autonomousvehicle.

BACKGROUND

Vehicles operating in an autonomous mode (e.g., driverless) can relieveoccupants, especially the driver, from some driving-relatedresponsibilities. When operating in an autonomous mode, the vehicle cannavigate to various locations using onboard sensors, allowing thevehicle to travel with minimal human interaction or in some caseswithout any passengers.

Vehicle calibration information is indispensable for longitudinalcontrol in the control system of an autonomous driving vehicle. Knownmanual calibration methods are not scalable.

SUMMARY

In an aspect of the disclosure, a computer-implemented method foroperating an autonomous driving vehicle (ADV) is provided. The methodincludes: issuing a control command to control the ADV, wherein thecontrol command is obtained from an entry of a calibration tableassociated with a speed and a desired acceleration of the ADV; measuringa first torque value in response to issuing the control command to theADV; determining a torque error value as a difference between the firsttorque value and a second torque value measured prior to issuing thecontrol command; and updating the associated entry in the calibrationtable based at least in part on the torque error value, wherein theupdated calibration table is utilized to determine subsequent controlcommands to control the ADV.

In another aspect of the disclosure, a non-transitory machine-readablemedium having instructions stored therein is provided. The instructionswhen executed by a processor cause the processor to perform operationsfor operating an autonomous driving vehicle (ADV), the operationsincluding issuing a control command to control the ADV, wherein thecontrol command is obtained from an entry of a calibration tableassociated with a speed and a desired acceleration of the ADV; measuringa first torque value in response to issuing the control command to theADV; determining a torque error value as a difference between the firsttorque value and a second torque value measured prior to issuing thecontrol command; and updating the associated entry in the calibrationtable based at least in part on the torque error value, wherein theupdated calibration table is utilized to determine subsequent controlcommands to control the ADV.

In another aspect of the disclosure, a data processing system isprovided. The system includes a processor; and memory coupled to theprocessor to store instructions, which when executed by the processor,cause the processor to perform operations for operating an autonomousdriving vehicle (ADV), the operations including: issuing a controlcommand to control the ADV, wherein the control command is obtained froman entry of a calibration table associated with a speed and a desiredacceleration of the ADV, measuring a first torque value in response toissuing the control command to the ADV, determining a torque error valueas a difference between the first torque value and a second torque valuemeasured prior to issuing the control command, and updating theassociated entry in the calibration table based at least in part on thetorque error value, wherein the updated calibration table is utilized todetermine subsequent control commands to control the ADV.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the disclosure are illustrated by way of example and notlimitation in the figures of the accompanying drawings in which likereferences indicate similar elements.

FIG. 1 is a block diagram illustrating a networked system according toone embodiment.

FIG. 2 is a block diagram illustrating an example of an autonomousvehicle according to one embodiment.

FIGS. 3A-3B are block diagrams illustrating an example of a perceptionand planning system used with an autonomous vehicle according to oneembodiment.

FIG. 4 shows an example of calibration table according to oneembodiment.

FIG. 5 is a block diagram illustrating various modules according to oneembodiment.

FIG. 6 is a flowchart illustrating an example method for updating acalibration table usable in operating an autonomous driving vehicle(ADV) according to one embodiment.

FIG. 7 is a block diagram illustrating a data processing systemaccording to one embodiment.

DETAILED DESCRIPTION

Various embodiments and aspects of the disclosures will be describedwith reference to details discussed below, and the accompanying drawingswill illustrate the various embodiments. The following description anddrawings are illustrative of the disclosure and are not to be construedas limiting the disclosure. Numerous specific details are described toprovide a thorough understanding of various embodiments of the presentdisclosure. However, in certain instances, well-known or conventionaldetails are not described in order to provide a concise discussion ofembodiments of the present disclosures.

Reference in the specification to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin conjunction with the embodiment can be included in at least oneembodiment of the disclosure. The appearances of the phrase “in oneembodiment” in various places in the specification do not necessarilyall refer to the same embodiment.

According to some embodiments, a calibration table usable in operatingan autonomous driving vehicle (ADV) is updated. First, a first torquevalue is determined at a first time instant prior to executing a controlcommand. A control command is determined based on a speed of the ADV, adesired acceleration, and an associated entry in the calibration table.The calibration table is used in the control of the speed of the ADV,and comprises a plurality of entries, each of which comprises a speed,an acceleration, and a control command comprising a throttle or brakepercentage. The associated entry is the entry whose speed corresponds tothe ADV's current speed and whose acceleration corresponds to a desiredacceleration. Thus, the calibration table yields a control command thatcomprises a throttle or brake percentage based on the current speed ofthe ADV and a desired acceleration for the ADV.

Next, the control command is executed. A second torque value isdetermined at a second time instant subsequent to executing the controlcommand. A torque error value is determined as a difference between thefirst and second torque values. Thereafter, the associated entry in thecalibration table is updated based at least in part on the torque errorvalue. Further, driving signals are generated based at least in part onthe updated calibration table to control operations of the ADV.

In one embodiment, updating the associated entry in the calibrationtable comprises updating the acceleration in the associated entry. Inone embodiment, the updated acceleration is determined based at least inpart on the acceleration in the associated entry prior to updating, thetorque error value, and a cost value. In one embodiment, the first orsecond torque value is determined based at least in part on an effectivemass of the ADV, an acceleration of the ADV, a mass of the ADV, a pitchangle of the ADV, a rolling resistance, an air drag coefficient, a speedof the ADV, and a tire radius of the ADV. In one embodiment, theeffective mass of the ADV is determined based at least in part on themass of the ADV, a moment of inertia of the ADV, and the tire radius ofthe ADV.

FIG. 1 is a block diagram illustrating an autonomous vehicle networkconfiguration according to one embodiment of the disclosure. Referringto FIG. 1, network configuration 100 includes autonomous vehicle 101that may be communicatively coupled to one or more servers 103-104 overa network 102. Although there is one autonomous vehicle shown, multipleautonomous vehicles can be coupled to each other and/or coupled toservers 103-104 over network 102. Network 102 may be any type ofnetworks such as a local area network (LAN), a wide area network (WAN)such as the Internet, a cellular network, a satellite network, or acombination thereof, wired or wireless. Server(s) 103-104 may be anykind of servers or a cluster of servers, such as Web or cloud servers,application servers, backend servers, or a combination thereof. Servers103-104 may be data analytics servers, content servers, trafficinformation servers, map and point of interest (MPOI) servers, orlocation servers, etc.

An autonomous vehicle refers to a vehicle that can be configured to inan autonomous mode in which the vehicle navigates through an environmentwith little or no input from a driver. Such an autonomous vehicle caninclude a sensor system having one or more sensors that are configuredto detect information about the environment in which the vehicleoperates. The vehicle and its associated controller(s) use the detectedinformation to navigate through the environment. Autonomous vehicle 101can operate in a manual mode, a full autonomous mode, or a partialautonomous mode.

In one embodiment, autonomous vehicle 101 includes, but is not limitedto, perception and planning system 110, vehicle control system 111,wireless communication system 112, user interface system 113,infotainment system 114, and sensor system 115. Autonomous vehicle 101may further include certain common components included in ordinaryvehicles, such as, an engine, wheels, steering wheel, transmission,etc., which may be controlled by vehicle control system 111 and/orperception and planning system 110 using a variety of communicationsignals and/or commands, such as, for example, acceleration signals orcommands, deceleration signals or commands, steering signals orcommands, braking signals or commands, etc.

Components 110-115 may be communicatively coupled to each other via aninterconnect, a bus, a network, or a combination thereof. For example,components 110-115 may be communicatively coupled to each other via acontroller area network (CAN) bus. A CAN bus is a vehicle bus standarddesigned to allow microcontrollers and devices to communicate with eachother in applications without a host computer. It is a message-basedprotocol, designed originally for multiplex electrical wiring withinautomobiles, but is also used in many other contexts.

Referring now to FIG. 2, in one embodiment, sensor system 115 includes,but it is not limited to, one or more cameras 211, global positioningsystem (GPS) unit 212, inertial measurement unit (IMU) 213, radar unit214, and a light detection and range (LIDAR) unit 215. GPS system 212may include a transceiver operable to provide information regarding theposition of the autonomous vehicle. IMU unit 213 may sense position andorientation changes of the autonomous vehicle based on inertialacceleration. Radar unit 214 may represent a system that utilizes radiosignals to sense objects within the local environment of the autonomousvehicle. In some embodiments, in addition to sensing objects, radar unit214 may additionally sense the speed and/or heading of the objects.LIDAR unit 215 may sense objects in the environment in which theautonomous vehicle is located using lasers. LIDAR unit 215 could includeone or more laser sources, a laser scanner, and one or more detectors,among other system components. Cameras 211 may include one or moredevices to capture images of the environment surrounding the autonomousvehicle. Cameras 211 may be still cameras and/or video cameras. A cameramay be mechanically movable, for example, by mounting the camera on arotating and/or tilting a platform.

Sensor system 115 may further include other sensors, such as, a sonarsensor, an infrared sensor, a steering sensor, a throttle sensor, abraking sensor, and an audio sensor (e.g., microphone). An audio sensormay be configured to capture sound from the environment surrounding theautonomous vehicle. A steering sensor may be configured to sense thesteering angle of a steering wheel, wheels of the vehicle, or acombination thereof. A throttle sensor and a braking sensor sense thethrottle position and braking position of the vehicle, respectively. Insome situations, a throttle sensor and a braking sensor may beintegrated as an integrated throttle/braking sensor.

In one embodiment, vehicle control system 111 includes, but is notlimited to, steering unit 201, throttle unit 202 (also referred to as anacceleration unit), and braking unit 203. Steering unit 201 is to adjustthe direction or heading of the vehicle. Throttle unit 202 is to controlthe speed of the motor or engine that in turn control the speed andacceleration of the vehicle. Braking unit 203 is to decelerate thevehicle by providing friction to slow the wheels or tires of thevehicle. Note that the components as shown in FIG. 2 may be implementedin hardware, software, or a combination thereof.

Referring back to FIG. 1, wireless communication system 112 is to allowcommunication between autonomous vehicle 101 and external systems, suchas devices, sensors, other vehicles, etc. For example, wirelesscommunication system 112 can wirelessly communicate with one or moredevices directly or via a communication network, such as servers 103-104over network 102. Wireless communication system 112 can use any cellularcommunication network or a wireless local area network (WLAN), e.g.,using WiFi to communicate with another component or system. Wirelesscommunication system 112 could communicate directly with a device (e.g.,a mobile device of a passenger, a display device, a speaker withinvehicle 101), for example, using an infrared link, Bluetooth, etc. Userinterface system 113 may be part of peripheral devices implementedwithin vehicle 101 including, for example, a keyboard, a touch screendisplay device, a microphone, and a speaker, etc.

Some or all of the functions of autonomous vehicle 101 may be controlledor managed by perception and planning system 110, especially whenoperating in an autonomous driving mode. Perception and planning system110 includes the necessary hardware (e.g., processor(s), memory,storage) and software (e.g., operating system, planning and routingprograms) to receive information from sensor system 115, control system111, wireless communication system 112, and/or user interface system113, process the received information, plan a route or path from astarting point to a destination point, and then drive vehicle 101 basedon the planning and control information. Alternatively, perception andplanning system 110 may be integrated with vehicle control system 111.

For example, a user as a passenger may specify a starting location and adestination of a trip, for example, via a user interface. Perception andplanning system 110 obtains the trip related data. For example,perception and planning system 110 may obtain location and routeinformation from an MPOI server, which may be a part of servers 103-104.The location server provides location services and the MPOI serverprovides map services and the POIs of certain locations. Alternatively,such location and MPOI information may be cached locally in a persistentstorage device of perception and planning system 110.

While autonomous vehicle 101 is moving along the route, perception andplanning system 110 may also obtain real-time traffic information from atraffic information system or server (TIS). Note that servers 103-104may be operated by a third party entity. Alternatively, thefunctionalities of servers 103-104 may be integrated with perception andplanning system 110. Based on the real-time traffic information, MPOIinformation, and location information, as well as real-time localenvironment data detected or sensed by sensor system 115 (e.g.,obstacles, objects, nearby vehicles), perception and planning system 110can plan an optimal route and drive vehicle 101, for example, viacontrol system 111, according to the planned route to reach thespecified destination safely and efficiently.

Server 103 may be a data analytics system to perform data analyticsservices for a variety of clients. In one embodiment, data analyticssystem 103 includes data collector 121 and machine learning engine 122.Data collector 121 collects driving statistics 123 from a variety ofvehicles, either autonomous vehicles or regular vehicles driven by humandrivers. Driving statistics 123 include information indicating thedriving commands (e.g., throttle, brake, steering commands) issued andresponses of the vehicles (e.g., speeds, accelerations, decelerations,directions) captured by sensors of the vehicles at different points intime. Driving statistics 123 may further include information describingthe driving environments at different points in time, such as, forexample, routes (including starting and destination locations), MPOIs,road conditions, weather conditions, etc.

Based on driving statistics 123, machine learning engine 122 generatesor trains a set of rules, algorithms, and/or predictive models 124 for avariety of purposes. In one embodiment, algorithms 124 may include analgorithm to automatically update a calibration table based on torquefeedback according to one embodiment. Algorithms 124 can then beuploaded on ADVs to be utilized during autonomous driving in real-time.

FIGS. 3A and 3B are block diagrams illustrating an example of aperception and planning system used with an autonomous vehicle accordingto one embodiment. System 300 may be implemented as a part of autonomousvehicle 101 of FIG. 1 including, but is not limited to, perception andplanning system 110, control system 111, and sensor system 115.Referring to FIGS. 3A-3B, perception and planning system 110 includes,but is not limited to, localization module 301, perception module 302,prediction module 303, decision module 304, planning module 305, controlmodule 306, routing module 307, and calibration module 308.

Some or all of modules 301-308 may be implemented in software, hardware,or a combination thereof. For example, these modules may be installed inpersistent storage device 352, loaded into memory 351, and executed byone or more processors (not shown). Note that some or all of thesemodules may be communicatively coupled to or integrated with some or allmodules of vehicle control system 111 of FIG. 2. Some of modules 301-308may be integrated together as an integrated module. For example,calibration module 308 may be implemented or integrated with controlmodule 306.

Localization module 301 determines a current location of autonomousvehicle 300 (e.g., leveraging GPS unit 212) and manages any data relatedto a trip or route of a user. Localization module 301 (also referred toas a map and route module) manages any data related to a trip or routeof a user. A user may log in and specify a starting location and adestination of a trip, for example, via a user interface. Localizationmodule 301 communicates with other components of autonomous vehicle 300,such as map and route information 311, to obtain the trip related data.For example, localization module 301 may obtain location and routeinformation from a location server and a map and POI (MPOI) server. Alocation server provides location services and an MPOI server providesmap services and the POIs of certain locations, which may be cached aspart of map and route information 311. While autonomous vehicle 300 ismoving along the route, localization module 301 may also obtainreal-time traffic information from a traffic information system orserver.

Based on the sensor data provided by sensor system 115 and localizationinformation obtained by localization module 301, a perception of thesurrounding environment is determined by perception module 302. Theperception information may represent what an ordinary driver wouldperceive surrounding a vehicle in which the driver is driving. Theperception can include the lane configuration, traffic light signals, arelative position of another vehicle, a pedestrian, a building,crosswalk, or other traffic related signs (e.g., stop signs, yieldsigns), etc., for example, in a form of an object. The laneconfiguration includes information describing a lane or lanes, such as,for example, a shape of the lane (e.g., straight or curvature), a widthof the lane, how many lanes in a road, one-way or two-way lane, mergingor splitting lanes, exiting lane, etc.

Perception module 302 may include a computer vision system orfunctionalities of a computer vision system to process and analyzeimages captured by one or more cameras in order to identify objectsand/or features in the environment of autonomous vehicle. The objectscan include traffic signals, road way boundaries, other vehicles,pedestrians, and/or obstacles, etc. The computer vision system may usean object recognition algorithm, video tracking, and other computervision techniques. In some embodiments, the computer vision system canmap an environment, track objects, and estimate the speed of objects,etc. Perception module 302 can also detect objects based on othersensors data provided by other sensors such as a radar and/or LIDAR.

For each of the objects, prediction module 303 predicts what the objectwill behave under the circumstances. The prediction is performed basedon the perception data perceiving the driving environment at the pointin time in view of a set of map/rout information 311 and traffic rules312. For example, if the object is a vehicle at an opposing directionand the current driving environment includes an intersection, predictionmodule 303 will predict whether the vehicle will likely move straightforward or make a turn. If the perception data indicates that theintersection has no traffic light, prediction module 303 may predictthat the vehicle may have to fully stop prior to enter the intersection.If the perception data indicates that the vehicle is currently at aleft-turn only lane or a right-turn only lane, prediction module 303 maypredict that the vehicle will more likely make a left turn or right turnrespectively.

For each of the objects, decision module 304 makes a decision regardinghow to handle the object. For example, for a particular object (e.g.,another vehicle in a crossing route) as well as its metadata describingthe object (e.g., a speed, direction, turning angle), decision module304 decides how to encounter the object (e.g., overtake, yield, stop,pass). Decision module 304 may make such decisions according to a set ofrules such as traffic rules or driving rules 312, which may be stored inpersistent storage device 352.

Routing module 307 is configured to provide one or more routes or pathsfrom a starting point to a destination point. For a given trip from astart location to a destination location, for example, received from auser, routing module 307 obtains route and map information 311 anddetermines all possible routes or paths from the starting location toreach the destination location. Routing module 307 may generate areference line in a form of a topographic map for each of the routes itdetermines from the starting location to reach the destination location.A reference line refers to an ideal route or path without anyinterference from others such as other vehicles, obstacles, or trafficcondition. That is, if there is no other vehicle, pedestrians, orobstacles on the road, an ADV should exactly or closely follows thereference line. The topographic maps are then provided to decisionmodule 304 and/or planning module 305. Decision module 304 and/orplanning module 305 examine all of the possible routes to select andmodify one of the most optimal routes in view of other data provided byother modules such as traffic conditions from localization module 301,driving environment perceived by perception module 302, and trafficcondition predicted by prediction module 303. The actual path or routefor controlling the ADV may be close to or different from the referenceline provided by routing module 307 dependent upon the specific drivingenvironment at the point in time.

Based on a decision for each of the objects perceived, planning module305 plans a path or route for the autonomous vehicle, as well as drivingparameters (e.g., distance, speed, and/or turning angle), using areference line provided by routing module 307 as a basis. That is, for agiven object, decision module 304 decides what to do with the object,while planning module 305 determines how to do it. For example, for agiven object, decision module 304 may decide to pass the object, whileplanning module 305 may determine whether to pass on the left side orright side of the object. Planning and control data is generated byplanning module 305 including information describing how vehicle 300would move in a next moving cycle (e.g., next route/path segment). Forexample, the planning and control data may instruct vehicle 300 to move10 meters at a speed of 30 mile per hour (mph), then change to a rightlane at the speed of 25 mph.

Based on the planning and control data, control module 306 controls anddrives the autonomous vehicle, by sending proper commands or signals tovehicle control system 111, according to a route or path defined by theplanning and control data. The planning and control data includesufficient information to drive the vehicle from a first point to asecond point of a route or path using appropriate vehicle settings ordriving parameters (e.g., throttle, braking, steering commands) atdifferent points in time along the path or route.

In one embodiment, the planning phase is performed in a number ofplanning cycles, also referred to as driving cycles, such as, forexample, in every time interval of 100 milliseconds (ms). For each ofthe planning cycles or driving cycles, one or more control commands willbe issued based on the planning and control data. That is, for every 100ms, planning module 305 plans a next route segment or path segment, forexample, including a target position and the time required for the ADVto reach the target position. Alternatively, planning module 305 mayfurther specify the specific speed, direction, and/or steering angle,etc. In one embodiment, planning module 305 plans a route segment orpath segment for the next predetermined period of time such as 5seconds. For each planning cycle, planning module 305 plans a targetposition for the current cycle (e.g., next 5 seconds) based on a targetposition planned in a previous cycle. Control module 306 then generatesone or more control commands (e.g., throttle, brake, steering controlcommands) based on the planning and control data of the current cycle.

Note that decision module 304 and planning module 305 may be integratedas an integrated module. Decision module 304/planning module 305 mayinclude a navigation system or functionalities of a navigation system todetermine a driving path for the autonomous vehicle. For example, thenavigation system may determine a series of speeds and directionalheadings to affect movement of the autonomous vehicle along a path thatsubstantially avoids perceived obstacles while generally advancing theautonomous vehicle along a roadway-based path leading to an ultimatedestination. The destination may be set according to user inputs viauser interface system 113. The navigation system may update the drivingpath dynamically while the autonomous vehicle is in operation. Thenavigation system can incorporate data from a GPS system and one or moremaps so as to determine the driving path for the autonomous vehicle.

According to one embodiment, calibration module 308 is configured tocalibrate or update calibration table 313 based on feedback receivedfrom the vehicle in response to various control commands issued to thevehicle automatically. FIG. 4 shows an example of calibration table 313.In one embodiment, when a control command, such as a throttle or brakecommand, is issued to the vehicle, a torque feedback is received fromthe vehicle via one or more sensors. Based on the torque feedback andthe torque value derived from the desired acceleration, an error ordifference between the torque feedback and the torque value can bedetermined. A corresponding acceleration value in the calibration tablecan be updated based on the torque error automatically using apredetermined algorithm.

The rationale behind this approach is that if there is an error in thecalibration table, a control command obtained from the calibration tablewould lead to an erroneous result, i.e., torque error. By measuring thedifference between the expected torque value and the actual feedbackmeasured from the vehicle, the calibration table can be adjusted. As aresult, a subsequent lookup can be performed with a lower error rate byissue proper control commands. By automatically update the calibrationtable based on the feedback measured at the point in time, thecalibration table can be adjusted or “trained” to fit to the specificdriving environment and/or specific type of vehicles.

Referring to FIG. 5, a block diagram 400 illustrating various modulesaccording to one embodiment is shown. A speed control module 410determines a desired acceleration 420 based on e.g., the output of theplanning module. A first torque value 432 is determined at a first timeinstant prior to executing a control command 440. A control command 440is determined based on a speed of the ADV 442, the desired acceleration420, and an associated entry in the calibration table 430, which may beimplemented as a part of calibration table 313 of FIGS. 3A and 3B. Thecalibration table 430 is used in the control of the speed of the ADV442, and comprises a plurality of entries, each of which comprises aspeed, an acceleration, and a control command comprising a throttle orbrake percentage, as shown in FIG. 4. In other words, the calibrationtable 420 with n entries can be represented as [v^(T),a^(T),cmd^(T)],where a=[a₁,a₂,a₃, . . . , a_(n)], v=[v₁,v₂,v₃, . . . , v_(n)], andcmd=[cmd₁,cmd₂,cmd₃, . . . , cmd_(n)].

The associated entry is the entry whose speed corresponds to the ADV442's current speed and whose acceleration corresponds to a desiredacceleration 420. Thus, the calibration table 430 yields a controlcommand 440 that includes a throttle or brake percentage based on thecurrent speed of the ADV 442 and a desired acceleration 420 for the ADV442. Specifically, referring now to FIG. 4, given a current speed of thevehicle and a desired acceleration, a lookup operation can be performedin calibration table 313 to locate an entry having speed field 461 andacceleration field 462 matching the current speed of the vehicle and thedesired acceleration. A control command can then be obtained from field463 of the matching entry.

Next, the control command 440 is executed. A second torque value 444 isdetermined at a second time instant subsequent to executing the controlcommand 440. A torque error value is determined as a difference betweenthe first and second torque values 432, 444. Thereafter, the associatedentry in the calibration table 430 is updated at the calibration tableupdate module 434 based at least in part on the torque error value.Further, driving signals are generated based at least in part on theupdated calibration table to control operations of the ADV.

In one embodiment, updating the associated entry in the calibrationtable comprises updating the acceleration in the associated entry. Theupdated acceleration is determined based at least in part on theacceleration in the associated entry prior to updating, the torque errorvalue, and a cost value. In particular, for example, the acceleration inthe i-th entry may be updated as follows:

${a_{i} = {a_{i}^{\prime} + \frac{e_{torque\_ error} \times \sigma}{1 + {cost}_{{cmd}_{i},v_{i}}}}},$where the cost value cost_(cmd) _(i) _(v) _(i)=α|cmd_(i)−cmd₀|+β|v_(i)−v₀|, where a_(i)′ is the acceleration prior toupdating, e_(torque_error) is the torque error value, and α, β, and σare tunable parameters that can be determined empirically. Command cmd0refers to the command that has just been executed and v0 refers to acurrent speed of the vehicle.

In one embodiment, the first or second torque value is determined basedat least in part on an effective mass of the ADV, an acceleration of theADV, a mass of the ADV, a pitch angle of the ADV, a rolling resistance,an air drag coefficient, a speed of the ADV, and a tire radius of theADV. The effective mass of the ADV is determined based at least in parton the mass of the ADV, a moment of inertia of the ADV, and the tireradius of the ADV. In particular, a torque value can be calculated asfollows: torque=(m_(eff)×a+mg cos(α)×C_(rr)+pv²)R, where a is theacceleration of the ADV, m is the mass of the ADV, g is thegravitational acceleration, α is the pitch angle, C_(rr) is the rollingresistance, p is the air drag coefficient, R is the tire radius, and theeffective mass of the ADV can be calculated as

${m_{{eff}\;} = {m + \frac{I}{R^{2}}}},$where I is moment of inertia of the ADV.

Referring to FIG. 6, a flowchart illustrating an example method 500 forupdating a calibration table usable in operating an autonomous drivingvehicle (ADV) according to one embodiment is shown. The method 500 canbe implemented in hardware, software, or a combination thereof. Themethod 500 can be implemented in part by the calibration module 308. Atblock 510, a control command is issued to an ADV, where the controlcommand was obtained from an entry a command calibration table. Theentry is associated with a speed of the ADV and a desired (or targeted)acceleration of the ADV. At block 520, a first torque value is measuredfrom the vehicle platform (e.g., using a various sensors). At block 530,a torque error value is calculated, where the torque error valuerepresents the difference between the first torque value and a secondtorque value measured prior to issuing the control command. At block540, the associated entry of the command calibration table is updatedbased on the torque error value. At block 550, the updated commandcalibration table is then utilized to obtain and issue subsequentcontrol commands.

Note that some or all of the components as shown and described above maybe implemented in software, hardware, or a combination thereof. Forexample, such components can be implemented as software installed andstored in a persistent storage device, which can be loaded and executedin a memory by a processor (not shown) to carry out the processes oroperations described throughout this application. Alternatively, suchcomponents can be implemented as executable code programmed or embeddedinto dedicated hardware such as an integrated circuit (e.g., anapplication specific IC or ASIC), a digital signal processor (DSP), or afield programmable gate array (FPGA), which can be accessed via acorresponding driver and/or operating system from an application.Furthermore, such components can be implemented as specific hardwarelogic in a processor or processor core as part of an instruction setaccessible by a software component via one or more specificinstructions.

FIG. 7 is a block diagram illustrating an example of a data processingsystem which may be used with one embodiment of the disclosure. Forexample, system 1500 may represent any of data processing systemsdescribed above performing any of the processes or methods describedabove, such as, for example, perception and planning system 110 or anyof servers 103-104 of FIG. 1. System 1500 can include many differentcomponents. These components can be implemented as integrated circuits(ICs), portions thereof, discrete electronic devices, or other modulesadapted to a circuit board such as a motherboard or add-in card of thecomputer system, or as components otherwise incorporated within achassis of the computer system.

Note also that system 1500 is intended to show a high level view of manycomponents of the computer system. However, it is to be understood thatadditional components may be present in certain implementations andfurthermore, different arrangement of the components shown may occur inother implementations. System 1500 may represent a desktop, a laptop, atablet, a server, a mobile phone, a media player, a personal digitalassistant (PDA), a Smartwatch, a personal communicator, a gaming device,a network router or hub, a wireless access point (AP) or repeater, aset-top box, or a combination thereof. Further, while only a singlemachine or system is illustrated, the term “machine” or “system” shallalso be taken to include any collection of machines or systems thatindividually or jointly execute a set (or multiple sets) of instructionsto perform any one or more of the methodologies discussed herein.

In one embodiment, system 1500 includes processor 1501, memory 1503, anddevices 1505-1508 connected via a bus or an interconnect 1510. Processor1501 may represent a single processor or multiple processors with asingle processor core or multiple processor cores included therein.Processor 1501 may represent one or more general-purpose processors suchas a microprocessor, a central processing unit (CPU), or the like. Moreparticularly, processor 1501 may be a complex instruction set computing(CISC) microprocessor, reduced instruction set computing (RISC)microprocessor, very long instruction word (VLIW) microprocessor, orprocessor implementing other instruction sets, or processorsimplementing a combination of instruction sets. Processor 1501 may alsobe one or more special-purpose processors such as an applicationspecific integrated circuit (ASIC), a cellular or baseband processor, afield programmable gate array (FPGA), a digital signal processor (DSP),a network processor, a graphics processor, a communications processor, acryptographic processor, a co-processor, an embedded processor, or anyother type of logic capable of processing instructions.

Processor 1501, which may be a low power multi-core processor socketsuch as an ultra-low voltage processor, may act as a main processingunit and central hub for communication with the various components ofthe system. Such processor can be implemented as a system on chip (SoC).Processor 1501 is configured to execute instructions for performing theoperations and steps discussed herein. System 1500 may further include agraphics interface that communicates with optional graphics subsystem1504, which may include a display controller, a graphics processor,and/or a display device.

Processor 1501 may communicate with memory 1503, which in one embodimentcan be implemented via multiple memory devices to provide for a givenamount of system memory. Memory 1503 may include one or more volatilestorage (or memory) devices such as random access memory (RAM), dynamicRAM (DRAM), synchronous DRAM (SDRAM), static RAM (SRAM), or other typesof storage devices. Memory 1503 may store information includingsequences of instructions that are executed by processor 1501, or anyother device. For example, executable code and/or data of a variety ofoperating systems, device drivers, firmware (e.g., input output basicsystem or BIOS), and/or applications can be loaded in memory 1503 andexecuted by processor 1501. An operating system can be any kind ofoperating systems, such as, for example, Robot Operating System (ROS),Windows® operating system from Microsoft®, Mac OS®/iOS® from Apple,Android® from Google®, LINUX, UNIX, or other real-time or embeddedoperating systems.

System 1500 may further include IO devices such as devices 1505-1508,including network interface device(s) 1505, optional input device(s)1506, and other optional IO device(s) 1507. Network interface device1505 may include a wireless transceiver and/or a network interface card(NIC). The wireless transceiver may be a WiFi transceiver, an infraredtransceiver, a Bluetooth transceiver, a WiMax transceiver, a wirelesscellular telephony transceiver, a satellite transceiver (e.g., a globalpositioning system (GPS) transceiver), or other radio frequency (RF)transceivers, or a combination thereof. The NIC may be an Ethernet card.

Input device(s) 1506 may include a mouse, a touch pad, a touch sensitivescreen (which may be integrated with display device 1504), a pointerdevice such as a stylus, and/or a keyboard (e.g., physical keyboard or avirtual keyboard displayed as part of a touch sensitive screen). Forexample, input device 1506 may include a touch screen controller coupledto a touch screen. The touch screen and touch screen controller can, forexample, detect contact and movement or break thereof using any of aplurality of touch sensitivity technologies, including but not limitedto capacitive, resistive, infrared, and surface acoustic wavetechnologies, as well as other proximity sensor arrays or other elementsfor determining one or more points of contact with the touch screen.

IO devices 1507 may include an audio device. An audio device may includea speaker and/or a microphone to facilitate voice-enabled functions,such as voice recognition, voice replication, digital recording, and/ortelephony functions. Other IO devices 1507 may further include universalserial bus (USB) port(s), parallel port(s), serial port(s), a printer, anetwork interface, a bus bridge (e.g., a PCI-PCI bridge), sensor(s)(e.g., a motion sensor such as an accelerometer, gyroscope, amagnetometer, a light sensor, compass, a proximity sensor, etc.), or acombination thereof. Devices 1507 may further include an imagingprocessing subsystem (e.g., a camera), which may include an opticalsensor, such as a charged coupled device (CCD) or a complementarymetal-oxide semiconductor (CMOS) optical sensor, utilized to facilitatecamera functions, such as recording photographs and video clips. Certainsensors may be coupled to interconnect 1510 via a sensor hub (notshown), while other devices such as a keyboard or thermal sensor may becontrolled by an embedded controller (not shown), dependent upon thespecific configuration or design of system 1500.

To provide for persistent storage of information such as data,applications, one or more operating systems and so forth, a mass storage(not shown) may also couple to processor 1501. In various embodiments,to enable a thinner and lighter system design as well as to improvesystem responsiveness, this mass storage may be implemented via a solidstate device (SSD). However in other embodiments, the mass storage mayprimarily be implemented using a hard disk drive (HDD) with a smalleramount of SSD storage to act as a SSD cache to enable non-volatilestorage of context state and other such information during power downevents so that a fast power up can occur on re-initiation of systemactivities. Also a flash device may be coupled to processor 1501, e.g.,via a serial peripheral interface (SPI). This flash device may providefor non-volatile storage of system software, including BIOS as well asother firmware of the system.

Storage device 1508 may include computer-accessible storage medium 1509(also known as a machine-readable storage medium or a computer-readablemedium) on which is stored one or more sets of instructions or software(e.g., module, unit, and/or logic 1528) embodying any one or more of themethodologies or functions described herein. Processingmodule/unit/logic 1528 may represent any of the components describedabove, such as, for example, planning module 305, control module 306,calibration module 308. Processing module/unit/logic 1528 may alsoreside, completely or at least partially, within memory 1503 and/orwithin processor 1501 during execution thereof by data processing system1500, memory 1503 and processor 1501 also constitutingmachine-accessible storage media. Processing module/unit/logic 1528 mayfurther be transmitted or received over a network via network interfacedevice 1505.

Computer-readable storage medium 1509 may also be used to store the somesoftware functionalities described above persistently. Whilecomputer-readable storage medium. 1509 is shown in an exemplaryembodiment to be a single medium, the term “computer-readable storagemedium” should be taken to include a single medium or multiple media(e.g., a centralized or distributed database, and/or associated cachesand servers) that store the one or more sets of instructions. The terms“computer-readable storage medium” shall also be taken to include anymedium that is capable of storing or encoding a set of instructions forexecution by the machine and that cause the machine to perform any oneor more of the methodologies of the present disclosure. The term“computer-readable storage medium” shall accordingly be taken toinclude, but not be limited to, solid-state memories, and optical andmagnetic media, or any other non-transitory machine-readable medium.

Processing module/unit/logic 1528, components and other featuresdescribed herein can be implemented as discrete hardware components orintegrated in the functionality of hardware components such as ASICS,FPGAs, DSPs or similar devices. In addition, processingmodule/unit/logic 1528 can be implemented as firmware or functionalcircuitry within hardware devices. Further, processing module/unit/logic1528 can be implemented in any combination hardware devices and softwarecomponents.

Note that while system 1500 is illustrated with various components of adata processing system, it is not intended to represent any particulararchitecture or manner of interconnecting the components; as suchdetails are not germane to embodiments of the present disclosure. Itwill also be appreciated that network computers, handheld computers,mobile phones, servers, and/or other data processing systems which havefewer components or perhaps more components may also be used withembodiments of the disclosure.

Some portions of the preceding detailed descriptions have been presentedin terms of algorithms and symbolic representations of operations ondata bits within a computer memory. These algorithmic descriptions andrepresentations are the ways used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the above discussion, itis appreciated that throughout the description, discussions utilizingterms such as those set forth in the claims below, refer to the actionand processes of a computer system, or similar electronic computingdevice, that manipulates and transforms data represented as physical(electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

Embodiments of the disclosure also relate to an apparatus for performingthe operations herein. Such a computer program is stored in anon-transitory computer readable medium. A machine-readable mediumincludes any mechanism for storing information in a form readable by amachine (e.g., a computer). For example, a machine-readable (e.g.,computer-readable) medium includes a machine (e.g., a computer) readablestorage medium (e.g., read only memory (“ROM”), random access memory(“RAM”), magnetic disk storage media, optical storage media, flashmemory devices).

The processes or methods depicted in the preceding figures may beperformed by processing logic that comprises hardware (e.g. circuitry,dedicated logic, etc.), software (e.g., embodied on a non-transitorycomputer readable medium), or a combination of both. Although theprocesses or methods are described above in terms of some sequentialoperations, it should be appreciated that some of the operationsdescribed may be performed in a different order. Moreover, someoperations may be performed in parallel rather than sequentially.

Embodiments of the present disclosure are not described with referenceto any particular programming language. It will be appreciated that avariety of programming languages may be used to implement the teachingsof embodiments of the disclosure as described herein.

In the foregoing specification, embodiments of the disclosure have beendescribed with reference to specific exemplary embodiments thereof. Itwill be evident that various modifications may be made thereto withoutdeparting from the broader spirit and scope of the disclosure as setforth in the following claims. The specification and drawings are,accordingly, to be regarded in an illustrative sense rather than arestrictive sense.

What is claimed is:
 1. A computer-implemented method for operating anautonomous driving vehicle (ADV), the method comprising: issuing acontrol command to control the ADV, wherein the control command isobtained from an entry of a calibration table associated with a speedand a desired acceleration of the ADV; measuring a first torque value inresponse to issuing the control command to the ADV; determining a torqueerror value as a difference between the first torque value and a secondtorque value measured prior to issuing the control command; and updatingthe associated entry in the calibration table based at least in part onthe torque error value, wherein the calibration table comprises aplurality of entries, each of which comprises an acceleration, whereinupdating the associated entry in the calibration table comprisesupdating the acceleration in the associated entry, wherein the updatedacceleration is determined based at least in part on the acceleration inthe associated entry prior to updating, the torque error value, and acost value, and wherein the cost value is determined based on a firstdifference between a control command of the associated entry and aprevious control command that was executed; and determining a subsequentcontrol commands based on the updated associated entry in thecalibration table based at least in part on the torque error value. 2.The method of claim 1, wherein the calibration table comprises aplurality of entries, each of which further comprises a speed and acontrol command comprising a throttle or brake percentage.
 3. The methodof claim 1, wherein the cost value is further determined based on asecond difference between a speed of the associated entry and a currentspeed of the ADV.
 4. The method of claim 1, wherein the first or secondtorque value is determined based at least in part on an effective massof the ADV, an acceleration of the ADV, a mass of the ADV, a pitch angleof the ADV, a rolling resistance, an air drag coefficient, a speed ofthe ADV, and a tire radius of the ADV.
 5. The method of claim 4, whereinthe effective mass of the ADV is determined based at least in part onthe mass of the ADV, a moment of inertia of the ADV, and the tire radiusof the ADV.
 6. The method of claim 1, further comprising generating thecalibration table to enable automatically calibrating the ADV based atleast in part on the torque error value.
 7. A non-transitorymachine-readable medium having instructions stored therein, which whenexecuted by a processor, cause the processor to perform operations foroperating an autonomous driving vehicle (ADV), the operationscomprising: issuing a control command to control the ADV, wherein thecontrol command is obtained from an entry of a calibration tableassociated with a speed and a desired acceleration of the ADV; measuringa first torque value in response to issuing the control command to theADV; determining a torque error value as a difference between the firsttorque value and a second torque value measured prior to issuing thecontrol command; and updating the associated entry in the calibrationtable based at least in part on the torque error value, wherein thecalibration table comprises a plurality of entries, each of whichcomprises an acceleration, wherein updating the associated entry in thecalibration table comprises updating the acceleration in the associatedentry, wherein the updated acceleration is determined based at least inpart on the acceleration in the associated entry prior to updating, thetorque error value, and a cost value, and wherein the cost value isdetermined based on a first difference between a control command of theassociated entry and a previous control command that was executed; anddetermining a subsequent control commands based on the updatedassociated entry in the calibration table based at least in part on thetorque error value.
 8. The machine-readable medium of claim 7, whereinthe calibration table comprises a plurality of entries, each of whichfurther comprises a speed and a control command comprising a throttle orbrake percentage.
 9. The machine-readable medium of claim 7, wherein thecost value is further determined based on a second difference between aspeed of the associated entry and a current speed of the ADV.
 10. Themachine-readable medium of claim 7, wherein the first or second torquevalue is determined based at least in part on an effective mass of theADV, an acceleration of the ADV, a mass of the ADV, a pitch angle of theADV, a rolling resistance, an air drag coefficient, a speed of the ADV,and a tire radius of the ADV.
 11. The machine-readable medium of claim10, wherein the effective mass of the ADV is determined based at leastin part on the mass of the ADV, a moment of inertia of the ADV, and thetire radius of the ADV.
 12. The machine-readable medium of claim 7,wherein the operations further comprise generating the calibration tableto enable automatically calibrating the ADV based at least in part onthe torque error value.
 13. A data processing system, comprising: aprocessor; and a memory coupled to the processor to store instructions,which when executed by the processor, cause the processor to performoperations for operating an autonomous driving vehicle (ADV), theoperations including: issuing a control command to control the ADV,wherein the control command is obtained from an entry of a calibrationtable associated with a speed and a desired acceleration of the ADV,measuring a first torque value in response to issuing the controlcommand to the ADV, determining a torque error value as a differencebetween the first torque value and a second torque value measured priorto issuing the control command, and updating the associated entry in thecalibration table based at least in part on the torque error value,wherein the calibration table comprises a plurality of entries, each ofwhich comprises an acceleration, wherein updating the associated entryin the calibration table comprises updating the acceleration in theassociated entry, wherein the updated acceleration is determined basedat least in part on the acceleration in the associated entry prior toupdating, the torque error value, and a cost value, and wherein the costvalue is determined based on a first difference between a controlcommand of the associated entry and a previous control command that wasexecuted; and determining a subsequent control commands based on theupdated associated entry in the calibration table based at least in parton the torque error value.
 14. The system of claim 13, wherein thecalibration table comprises a plurality of entries, each of whichfurther comprises a speed and a control command comprising a throttle orbrake percentage.
 15. The system of claim 13, wherein the cost value isfurther determined based on a second difference between a speed of theassociated entry and a current speed of the ADV.
 16. The system of claim13, wherein the first or second torque value is determined based atleast in part on an effective mass of the ADV, an acceleration of theADV, a mass of the ADV, a pitch angle of the ADV, a rolling resistance,an air drag coefficient, a speed of the ADV, and a tire radius of theADV.
 17. The system of claim 16, wherein the effective mass of the ADVis determined based at least in part on the mass of the ADV, a moment ofinertia of the ADV, and the tire radius of the ADV.
 18. The system ofclaim 13, wherein the operations further comprise generating thecalibration table to enable automatically calibrating the ADV based atleast in part on the torque error value.